A New Approach for Completing Missing Data Series in Pan Evaporation Using Multi-Meteorologic Phenomena

The most crucial losses in the hydrological cycle occur due to evaporation (EP). As a result, the accurate attainment of this complex phenomenon is critical in studies on irrigation, efficiency in the basins, dams, continuous hydrometeorological simulations, flood frequency, and water budget analysis. However, EP data sets are expensive, difficult to sustainably measure, and scarce, also, predictions are challenging tasks due to the wide range of parameters involved in these processes. In this study, the data gaps are filled with Class A evaporation pan observations through building a new meteorological station during seasons with no gauge measurements available for a three-year time period. These observations demonstrate high correlations with the readings from the Meteorology Airport Station, with a PCC of 0.75. After the continuous EP time series was completed over Kahramanmaras, these values were retrieved non-linearly via an artificial intelligence model using multi-meteorological parameters. In the study, the simulation performance is evaluated with the help of eight different statistical metrics in addition to graphical representations. The evaluation reveals that, when compared to the other EP functions, using both temperature and wind-driven simulations has the highest correlation (PCC = 0.94) and NSCE (0.87), as well as the lowest bias (PBias = -1.65%, MAE = 1.27 mm d-1, RMSD = 1.6 mm d-1, CRMSE = 24%) relative to the gauge measurements, while they give the opposite results in the solely precipitation-based models (PCC = 0.42, NSCE = 0.17, PBias = -6.44%, MAE = 3.58 mm d-1, RMSD = 4.2 mm d-1, CRMSE = 62%). It has been clearly seen that the temperature parameter is the most essential factor, while precipitation alone may be insufficient in EP predictions; additionally, wind speed and relative humidity would improve the prediction performance in artificial intelligence techniques.

Automated summary

The study fills data gaps by establishing a new meteorological station and using Class A evaporation pan observations, and utilizes an artificial intelligence model to process the continuous evaporation data nonlinearly. Evaluation shows that temperature and wind-driven simulations have the highest correlation and performance compared to solely precipitation-based models in predicting evaporation.